Cohere is Good to agents.
Discry independently scored how well an AI agent can discover and understand the Cohere API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.
SCORED UNDER RUBRIC 1.2 · A full re-launch under Discry Score 2.5 — a new behavioral instrument, not comparable to these scores — is in progress.
Discovery
45% of score · 76/100Comprehension
55% of score · 100/100What we found
- An agent looking for Cohere finds it instantly and parses it cleanly: a root llms.txt with explicit AI-agent instructions, .md append on every page, per-section /llms.txt indexes, and a hosted docs MCP server (docs.cohere.com/_mcp/server) make these docs unusually agent-native.
- Comprehension is exemplary — task-oriented endpoint descriptions, four-language realistic code examples (Python/TypeScript/Java/Go), consistent snake_case fields, and granular per-model rate-limit tables mean an agent can call the API with almost no guesswork.
- Error handling is genuinely agent-usable: every status code (400/401/402/404/429/499/500) pairs an explicit resolution step with specific, often self-correcting example messages (e.g. "setting prompt_truncation='AUTO'" or "add or update your payment method" with a direct link).
- The single biggest discovery gap is AGENTS.md — absent from both the docs repo (cohere-developer-experience) and the SDK repos (cohere-python) — so a coding agent that clones the SDK gets no repo-level orientation, and it costs the most discovery weight.
- Standard MCP discovery is only partial: there is no /.well-known/mcp.json despite a working docs MCP server, and Cohere shows up in MCP registries mainly through third-party integrations rather than an official first-party listing.
What to change
Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.
- 01Add an AGENTS.md to cohere-python (and the docs monorepo) — the highest-impact discovery fix (weight 3): orient coding agents on installation, the CO_API_KEY env var, and the ClientV2() entry point.
- 02Publish /.well-known/mcp.json pointing at the existing docs MCP server (docs.cohere.com/_mcp/server) so agents can auto-discover it via the standard well-known path instead of only finding it inside llms.txt.
- 03Turn the root /llms-full.txt into a true comprehensive markdown dump — it currently mirrors the tiny llms.txt index — so an agent can pull full API context in a single fetch.
- 04Get an official Cohere MCP server listed in Glama, Smithery, or PulseMCP to convert the partial registry signal into a pass.
Execution coverage · INFORMATIONAL, UNSCORED
Whether an agent can actually complete a call and recover from errors is the deeper Audit layer — documented here, but not part of the Discry Score.
Cohere documents API-key (Bearer) authentication plus an OpenAI-SDK compatibility layer, a machine-parseable JSON error format with per-status-code descriptions and specific example messages, and granular per-model and per-endpoint rate-limit tables (trial vs production). Pagination is not prominently documented for the core endpoints (chat/rerank/embed do not paginate) and idempotency keys are not mentioned.